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Regression, Sources, When should I use MAE vs RMSE? How do I know if my…
Regression
Regression Models
Ridge Regression
reduces overfitting with penalty
Polynomial Regression
fits a curved line
LASSO Regression
can remove features
Linear Regression
fits a straight line
Evaluation Metrics
Mean Squared Error (MSE)
squares errors
Root Mean Squared Error (RMSE)
sqrt of MSE
Mean Absolute Error (MAE)
average absolute error
R-squared
variance explained
Input Features & Output Targets
Input Features (X)
data used to make predictions
Output Targets (y)
value being predicted
Model Fit
Overfitting
model too complex
Underfitting
model too simple
Baseline Models
Dummy Regressors
simple baseline prediction
Sources
ChatGpt
IBM: Regression Analysis overview
When should I use MAE vs RMSE?
How do I know if my model is overfitting?